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Indicating interdisciplinarity in AI

Indicating interdisciplinarity in AI

A one-day workshop at the University of Warwick funded by The Alan Turing Institute.

6 February 2020

Radcliffe Conference Centre

Hosted by: The Centre for Interdisciplinary Methodologies (CIM), University of Warwick
in collaboration with the Centre for Science & Technology Studies (CWTS), Leiden University

data studio capture

Recent interest, investment in and concern with artificial intelligence, as a strategic area of research and innovation, is connecting disciplines across the sciences and humanities in potentially new ways. Much is expected, in terms of the contribution to knowledge, economy and society, from new applications of computational methods such as machine learning to social and cultural topics. This has led some thinkers to express concern that the capacity to know society and culture is being shifted to disciplines with little experience and training in the distinctive challenges that have been associated with these projects. However, scholars and scientists have also argued that recent developments in artificial intelligence make possible fundamental transformations of the relations between the sciences, social sciences and humanities, enabling new ways of combining approaches, ontologies and methodologies, which have historically been considered incommensurable (Rawhan et al, 2019; Castelle, forthcoming).1 In this workshop, we would like to examine these challenges and opportunities for interdisciplinary exchange in AI research and innovation with the aid of a diverse and experimental set of evaluative methods: scientometrics, evaluative enquiry, data visualisation and playful methods.

Workshop focus and program

The workshop builds on an on-going collaboration between the Universities of Leiden and Warwick "Inventing Indicators of Interdisciplinarity." This experimental pilot project aims is to make links between scientometrics, data visualisation, evaluative inquiry and playful methods (design research) in order to devise alternative, more relational approaches to interdisciplinarity and its e-valuation. To advance this wider project, this workshop will present, explore and test the capacity of the above evaluative methods to open up for investigation - and constitute as matters of reflection (Venturini, 2019) - the relations between disciplines in AI research and innovation.

Thus, we ask: what areas of machine learning - neural networks, topic modelling, .. - are most productive in terms of enabling exchange between science and engineering-based, and social and cultural approaches? Is it the case that interdisciplinary AI is mainly concerned with the application of computational methods to social and cultural questions? What other forms of collaboration across disciplines can we detect in AI as an interdisciplinary area of research and innovation? And what is the potential contribution that new types of disciplinary and interdisciplinary ensembles seek to make in this area?

To address these questions, the workshop willl combine different formats, specifically, presentations, roundtable discussion and participatory prototyping. We begin by reviewing how scientometric methods (micro-fields; heterogeneous coupling) enable a relational analysis of interdisciplinarity in AI research and innovation. We then discuss what contribution more experimental evaluative methods - such as media cartography and data visualisation - can make to the understanding of interdisciplinarity in AI. Finally, in a practice-based exercise we examine whether and how interdisciplinarity indicators can be used to invite interested parties - scholars, scientists, industry and civil society representatives - into the evaluation of AI as an interdisciplinary area of research and innovation.

Participants

Anne Beaulieu (University of Groningen)
Gian Marco Campagnolo (University of Edinburgh)
Michael Castelle (University of Warwick)
Rodrigo Costas Comesana (University of Stellenbosch/Leiden University)
Thomas Franssen (Leiden University)
Tjitkse Holtrop (Leiden University)
Karol Kurnicki (University of Warwick)
Sybille Lammes (Leiden University)
Celia Lury (University of Warwick)
Noortje Marres (University of Warwick)
Greg McInerny (University of Warwick)
Rob Proctor (University of Warwick) - TBC
Ismael Rafols (SPRU - University of Sussex/ Leiden University)
Sarah de Rijcke (Leiden University)
Matt Spencer (University of Warwick)
Alexander Stingl (University of Warwick)
Cagatay Turkay (University of Warwick)
Tommaso Venturini (CNRS, Internet and Society)
Ludo Waltman (Leiden University)
Scott Wark (University of Warwick)

This event is sponsored by The Alan Turing Institute, the national intitute for data science and artificial intelligence. Find out more at turing.ac.uk

For more info, please email Kanishka Mathiarasan, CIM Research Administrator, at Kanishka.Mathiarasan@warwick.ac.uk

Image credit: Data Studio Mock-up (L.Kimbell, 2017)


1https://www.nature.com/articles/s41586-019-1138-y